CODEWITHSUNDEEP
opencvimage-processingfilterfeature-detectionsurf
kcc__
kcc__

Reputation: 1648

OpenCV Textured object Tracking Using Filter

I am working on a project for tracking objects with many textures with many color variations. Unlike normal tracking using color masking, my target object is vastly different since it contains several dominant color distribution.

I want to detect the object and start tracking. For tracking there are methods like Kalman Filter but however, to track textured object, I need to track the local keypoints I suppose. If I compute SURF descriptor in every frame, than this will forfeit the use of tracking I think(maybe I am wrong here).

I feel that I should just use SURF in the first frame to initialised the target object and than thereafter track it. My problem here is how do I track the features of the textured object. I done some research but I seem not to get how to go about solving that.

It would be great if I can get some suggestion or paper links to read and implement it.

Upvotes: 0

Views: 1066

Answers (1)

GilLevi
GilLevi

Reputation: 2137

You can try binary descriptors, they are much more efficient then SURF in both extracting and comparing. Perhaps using less keypoints and describing them with a binary descriptor will work in real time. Also, when tracking you'll have an estimate of the object's positions, so maybe you don't have to compute descriptors in the entire image, but only at the estimated object's position.

I wrote a tutorial about binary descriptors.

part 1 - Introduction: http://gilscvblog.wordpress.com/2013/08/26/tutorial-on-binary-descriptors-part-1/

part 2 - the BRIEF descriptor: http://gilscvblog.wordpress.com/2013/09/19/a-tutorial-on-binary-descriptors-part-2-the-brief-descriptor/

part 3 - the ORB descriptor: http://gilscvblog.wordpress.com/2013/10/04/a-tutorial-on-binary-descriptors-part-3-the-orb-descriptor/

part 4 - the BRISK descriptor: http://gilscvblog.wordpress.com/2013/11/08/a-tutorial-on-binary-descriptors-part-4-the-brisk-descriptor/

Upvotes: 1

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